

'''simple docstring'''

import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE							:				int     	=			get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
							from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

SCREAMING_SNAKE_CASE							:				Any     	=			256047
SCREAMING_SNAKE_CASE							:				Union[str, Any]     	=			256145






@require_sentencepiece
@require_tokenizers
class    snake_case     (      snake_case_,						unittest.TestCase		):






				"""simple docstring"""
				_a    	=     NllbTokenizer
				_a    	=     NllbTokenizerFast
				_a    	=     True
				_a    	=     True
				_a    	=     {}



				def   a__					(    self       )     ->							Any:
											super().setUp()

											# We have a SentencePiece fixture for testing
											SCREAMING_SNAKE_CASE_				=				NllbTokenizer(_lowercase,     keep_accents=_lowercase       )
											tokenizer.save_pretrained(self.tmpdirname       )



				def   a__					(    self       )     ->							List[Any]:
											SCREAMING_SNAKE_CASE_				=				NllbTokenizer(_lowercase,     keep_accents=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				tokenizer.tokenize('This is a test'       )
											self.assertListEqual(_lowercase,     ['▁This', '▁is', '▁a', '▁t', 'est']       )

											self.assertListEqual(
											    tokenizer.convert_tokens_to_ids(_lowercase       ),     [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]],     )

											SCREAMING_SNAKE_CASE_				=				tokenizer.tokenize('I was born in 92000, and this is falsé.'       )
											self.assertListEqual(
											    _lowercase,     [
											        SPIECE_UNDERLINE + 'I',
											        SPIECE_UNDERLINE + 'was',
											        SPIECE_UNDERLINE + 'b',
											        'or',
											        'n',
											        SPIECE_UNDERLINE + 'in',
											        SPIECE_UNDERLINE + '',
											        '9',
											        '2',
											        '0',
											        '0',
											        '0',
											        ',',
											        SPIECE_UNDERLINE + 'and',
											        SPIECE_UNDERLINE + 'this',
											        SPIECE_UNDERLINE + 'is',
											        SPIECE_UNDERLINE + 'f',
											        'al',
											        's',
											        'é',
											        '.',
											    ],     )
											SCREAMING_SNAKE_CASE_				=				tokenizer.convert_tokens_to_ids(_lowercase       )
											self.assertListEqual(
											    _lowercase,     [
											        value + tokenizer.fairseq_offset
											        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
											    ],     )

											SCREAMING_SNAKE_CASE_				=				tokenizer.convert_ids_to_tokens(_lowercase       )
											self.assertListEqual(
											    _lowercase,     [
											        SPIECE_UNDERLINE + 'I',
											        SPIECE_UNDERLINE + 'was',
											        SPIECE_UNDERLINE + 'b',
											        'or',
											        'n',
											        SPIECE_UNDERLINE + 'in',
											        SPIECE_UNDERLINE + '',
											        '<unk>',
											        '2',
											        '0',
											        '0',
											        '0',
											        ',',
											        SPIECE_UNDERLINE + 'and',
											        SPIECE_UNDERLINE + 'this',
											        SPIECE_UNDERLINE + 'is',
											        SPIECE_UNDERLINE + 'f',
											        'al',
											        's',
											        '<unk>',
											        '.',
											    ],     )



				def   a__					(    self       )     ->							Dict:
											SCREAMING_SNAKE_CASE_				=				(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-nllb', {})
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
																									SCREAMING_SNAKE_CASE_				=				self.rust_tokenizer_class.from_pretrained(_lowercase,     **_lowercase       )
																									SCREAMING_SNAKE_CASE_				=				self.tokenizer_class.from_pretrained(_lowercase,     **_lowercase       )

																									SCREAMING_SNAKE_CASE_				=				tempfile.mkdtemp()

																									SCREAMING_SNAKE_CASE_				=				tokenizer_r.save_pretrained(_lowercase       )
																									SCREAMING_SNAKE_CASE_				=				tokenizer_p.save_pretrained(_lowercase       )

																									# Checks it save with the same files + the tokenizer.json file for the fast one
																									self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files       )       )
																									SCREAMING_SNAKE_CASE_				=				tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f       )
																									self.assertSequenceEqual(_lowercase,     _lowercase       )

																									# Checks everything loads correctly in the same way
																									SCREAMING_SNAKE_CASE_				=				tokenizer_r.from_pretrained(_lowercase       )
																									SCREAMING_SNAKE_CASE_				=				tokenizer_p.from_pretrained(_lowercase       )

																									# Check special tokens are set accordingly on Rust and Python
																									for key in tokenizer_pp.special_tokens_map:
																																self.assertTrue(hasattr(_lowercase,     _lowercase       )       )

																									shutil.rmtree(_lowercase       )

																									# Save tokenizer rust, legacy_format=True
																									SCREAMING_SNAKE_CASE_				=				tempfile.mkdtemp()

																									SCREAMING_SNAKE_CASE_				=				tokenizer_r.save_pretrained(_lowercase,     legacy_format=_lowercase       )
																									SCREAMING_SNAKE_CASE_				=				tokenizer_p.save_pretrained(_lowercase       )

																									# Checks it save with the same files
																									self.assertSequenceEqual(_lowercase,     _lowercase       )

																									# Checks everything loads correctly in the same way
																									SCREAMING_SNAKE_CASE_				=				tokenizer_r.from_pretrained(_lowercase       )
																									SCREAMING_SNAKE_CASE_				=				tokenizer_p.from_pretrained(_lowercase       )

																									# Check special tokens are set accordingly on Rust and Python
																									for key in tokenizer_pp.special_tokens_map:
																																self.assertTrue(hasattr(_lowercase,     _lowercase       )       )

																									shutil.rmtree(_lowercase       )

																									# Save tokenizer rust, legacy_format=False
																									SCREAMING_SNAKE_CASE_				=				tempfile.mkdtemp()

																									SCREAMING_SNAKE_CASE_				=				tokenizer_r.save_pretrained(_lowercase,     legacy_format=_lowercase       )
																									SCREAMING_SNAKE_CASE_				=				tokenizer_p.save_pretrained(_lowercase       )

																									# Checks it saved the tokenizer.json file
																									self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files       )       )

																									# Checks everything loads correctly in the same way
																									SCREAMING_SNAKE_CASE_				=				tokenizer_r.from_pretrained(_lowercase       )
																									SCREAMING_SNAKE_CASE_				=				tokenizer_p.from_pretrained(_lowercase       )

																									# Check special tokens are set accordingly on Rust and Python
																									for key in tokenizer_pp.special_tokens_map:
																																self.assertTrue(hasattr(_lowercase,     _lowercase       )       )

																									shutil.rmtree(_lowercase       )



				@require_torch
				def   a__					(    self       )     ->							Tuple:
											if not self.test_seqaseq:
																		return

											SCREAMING_SNAKE_CASE_				=				self.get_tokenizers()
											for tokenizer in tokenizers:
																		with self.subTest(f"""{tokenizer.__class__.__name__}"""       ):
																									# Longer text that will definitely require truncation.
																									SCREAMING_SNAKE_CASE_				=				[
																									    ' UN Chief Says There Is No Military Solution in Syria',
																									    ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'
																									    ' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'
																									    ' will only worsen the violence and misery for millions of people.',
																									]
																									SCREAMING_SNAKE_CASE_				=				[
																									    'Şeful ONU declară că nu există o soluţie militară în Siria',
																									    'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'
																									    ' Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi'
																									    ' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
																									]
																									try:
																																SCREAMING_SNAKE_CASE_				=				tokenizer.prepare_seqaseq_batch(
																																    src_texts=_lowercase,     tgt_texts=_lowercase,     max_length=3,     max_target_length=10,     return_tensors='pt',     src_lang='eng_Latn',     tgt_lang='ron_Latn',     )
																									except NotImplementedError:
																																return
																									self.assertEqual(batch.input_ids.shape[1],     3       )
																									self.assertEqual(batch.labels.shape[1],     10       )
																									# max_target_length will default to max_length if not specified
																									SCREAMING_SNAKE_CASE_				=				tokenizer.prepare_seqaseq_batch(
																									    _lowercase,     tgt_texts=_lowercase,     max_length=3,     return_tensors='pt'       )
																									self.assertEqual(batch.input_ids.shape[1],     3       )
																									self.assertEqual(batch.labels.shape[1],     3       )

																									SCREAMING_SNAKE_CASE_				=				tokenizer.prepare_seqaseq_batch(
																									    src_texts=_lowercase,     max_length=3,     max_target_length=10,     return_tensors='pt'       )
																									self.assertEqual(batch_encoder_only.input_ids.shape[1],     3       )
																									self.assertEqual(batch_encoder_only.attention_mask.shape[1],     3       )
																									self.assertNotIn('decoder_input_ids',     _lowercase       )



				@unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.'       )
				def   a__					(    self       )     ->							Union[str, Any]:
											pass





				def   a__					(    self       )     ->							int:
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
																									SCREAMING_SNAKE_CASE_				=				[AddedToken('<special>',     lstrip=_lowercase       )]

																									SCREAMING_SNAKE_CASE_				=				self.rust_tokenizer_class.from_pretrained(
																									    _lowercase,     additional_special_tokens=_lowercase,     **_lowercase       )
																									SCREAMING_SNAKE_CASE_				=				tokenizer_r.encode('Hey this is a <special> token'       )

																									SCREAMING_SNAKE_CASE_				=				tokenizer_r.encode('<special>',     add_special_tokens=_lowercase       )[0]

																									self.assertTrue(special_token_id in r_output       )

																									if self.test_slow_tokenizer:
																																SCREAMING_SNAKE_CASE_				=				self.rust_tokenizer_class.from_pretrained(
																																    _lowercase,     additional_special_tokens=_lowercase,     **_lowercase,     )
																																SCREAMING_SNAKE_CASE_				=				self.tokenizer_class.from_pretrained(
																																    _lowercase,     additional_special_tokens=_lowercase,     **_lowercase       )

																																SCREAMING_SNAKE_CASE_				=				tokenizer_p.encode('Hey this is a <special> token'       )

																																SCREAMING_SNAKE_CASE_				=				tokenizer_cr.encode('Hey this is a <special> token'       )

																																self.assertEqual(_lowercase,     _lowercase       )
																																self.assertEqual(_lowercase,     _lowercase       )
																																self.assertTrue(special_token_id in p_output       )
																																self.assertTrue(special_token_id in cr_output       )






@require_torch
@require_sentencepiece
@require_tokenizers
class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""
				_a    	=     """facebook/nllb-200-distilled-600M"""
				_a    	=     [
				    """ UN Chief Says There Is No Military Solution in Syria""",
				    """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
				]
				_a    	=     [
				    """Şeful ONU declară că nu există o soluţie militară în Siria""",
				    """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
				    """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
				    """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
				]
				_a    	=     [
				    25_6047,
				    1_6297,
				    13_4408,
				    8165,
				    24_8066,
				    1_4734,
				    950,
				    1135,
				    10_5721,
				    3573,
				    83,
				    2_7352,
				    108,
				    4_9486,
				    2,
				]



				@classmethod
				def   a__					(    cls       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				NllbTokenizer.from_pretrained(
											    cls.checkpoint_name,     src_lang='eng_Latn',     tgt_lang='ron_Latn'       )
											SCREAMING_SNAKE_CASE_				=				1
											return cls



				def   a__					(    self       )     ->							List[str]:
											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab'],     256001       )
											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn'],     256002       )
											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn'],     256057       )



				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				self.tokenizer.batch_encode_plus(self.src_text       ).input_ids[0]
											self.assertListEqual(self.expected_src_tokens,     _lowercase       )



				def   a__					(    self       )     ->							Any:
											self.assertIn(_lowercase,     self.tokenizer.all_special_ids       )
											# fmt: off
											SCREAMING_SNAKE_CASE_				=				[RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047]
											# fmt: on

											SCREAMING_SNAKE_CASE_				=				self.tokenizer.decode(_lowercase,     skip_special_tokens=_lowercase       )
											SCREAMING_SNAKE_CASE_				=				self.tokenizer.decode(generated_ids[1:],     skip_special_tokens=_lowercase       )
											self.assertEqual(_lowercase,     _lowercase       )
											self.assertNotIn(self.tokenizer.eos_token,     _lowercase       )



				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				['this is gunna be a long sentence ' * 20]
											assert isinstance(src_text[0],     _lowercase       )
											SCREAMING_SNAKE_CASE_				=				10
											SCREAMING_SNAKE_CASE_				=				self.tokenizer(_lowercase,     max_length=_lowercase,     truncation=_lowercase       ).input_ids[0]
											self.assertEqual(ids[-1],     2       )
											self.assertEqual(ids[0],     _lowercase       )
											self.assertEqual(len(_lowercase       ),     _lowercase       )



				def   a__					(    self       )     ->							Union[str, Any]:
											self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']       ),     [256203, 3]       )



				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				tempfile.mkdtemp()
											SCREAMING_SNAKE_CASE_				=				self.tokenizer.fairseq_tokens_to_ids
											self.tokenizer.save_pretrained(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				NllbTokenizer.from_pretrained(_lowercase       )
											self.assertDictEqual(new_tok.fairseq_tokens_to_ids,     _lowercase       )



				@require_torch
				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				self.tokenizer(
											    self.src_text,     text_target=self.tgt_text,     padding=_lowercase,     truncation=_lowercase,     max_length=len(self.expected_src_tokens       ),     return_tensors='pt',     )
											SCREAMING_SNAKE_CASE_				=				shift_tokens_right(
											    batch['labels'],     self.tokenizer.pad_token_id,     self.tokenizer.lang_code_to_id['ron_Latn']       )

											self.assertIsInstance(_lowercase,     _lowercase       )

											self.assertEqual((2, 15),     batch.input_ids.shape       )
											self.assertEqual((2, 15),     batch.attention_mask.shape       )
											SCREAMING_SNAKE_CASE_				=				batch.input_ids.tolist()[0]
											self.assertListEqual(self.expected_src_tokens,     _lowercase       )
											self.assertEqual(_lowercase,     batch.decoder_input_ids[0, 0]       )  # EOS
											# Test that special tokens are reset
											self.assertEqual(self.tokenizer.prefix_tokens,     [EN_CODE]       )
											self.assertEqual(self.tokenizer.suffix_tokens,     [self.tokenizer.eos_token_id]       )



				def   a__					(    self       )     ->							Dict:
											SCREAMING_SNAKE_CASE_				=				self.tokenizer(self.src_text,     padding=_lowercase,     truncation=_lowercase,     max_length=3,     return_tensors='pt'       )
											SCREAMING_SNAKE_CASE_				=				self.tokenizer(
											    text_target=self.tgt_text,     padding=_lowercase,     truncation=_lowercase,     max_length=10,     return_tensors='pt'       )
											SCREAMING_SNAKE_CASE_				=				targets['input_ids']
											SCREAMING_SNAKE_CASE_				=				shift_tokens_right(
											    _lowercase,     self.tokenizer.pad_token_id,     decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang],     )

											self.assertEqual(batch.input_ids.shape[1],     3       )
											self.assertEqual(batch.decoder_input_ids.shape[1],     10       )



				@require_torch
				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				self.tokenizer._build_translation_inputs(
											    'A test',     return_tensors='pt',     src_lang='eng_Latn',     tgt_lang='fra_Latn'       )

											self.assertEqual(
											    nested_simplify(_lowercase       ),     {
											        # A, test, EOS, en_XX
											        'input_ids': [[256047, 70, 7356, 2]],
											        'attention_mask': [[1, 1, 1, 1]],
											        # ar_AR
											        'forced_bos_token_id': 256057,
											    },     )





				@require_torch
				def   a__					(    self       )     ->							Dict:
											SCREAMING_SNAKE_CASE_				=				True
											SCREAMING_SNAKE_CASE_				=				self.tokenizer(
											    'UN Chief says there is no military solution in Syria',     src_lang='eng_Latn',     tgt_lang='fra_Latn'       )
											self.assertEqual(
											    inputs.input_ids,     [16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2, 256047]       )

											SCREAMING_SNAKE_CASE_				=				False
											SCREAMING_SNAKE_CASE_				=				self.tokenizer(
											    'UN Chief says there is no military solution in Syria',     src_lang='eng_Latn',     tgt_lang='fra_Latn'       )
											self.assertEqual(
											    inputs.input_ids,     [256047, 16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2]       )






"""simple docstring"""



import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			"""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
SCREAMING_SNAKE_CASE__:List[str]									=			direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
SCREAMING_SNAKE_CASE__:Optional[int]									=			re.compile(R"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
SCREAMING_SNAKE_CASE__:Optional[Any]									=			re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
SCREAMING_SNAKE_CASE__:int									=			[
    ("""pretraining""", """MODEL_FOR_PRETRAINING_MAPPING_NAMES""", """AutoModelForPreTraining"""),
    ("""feature-extraction""", """MODEL_MAPPING_NAMES""", """AutoModel"""),
    ("""audio-classification""", """MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForAudioClassification"""),
    ("""text-generation""", """MODEL_FOR_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForCausalLM"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_CTC_MAPPING_NAMES""", """AutoModelForCTC"""),
    ("""image-classification""", """MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForImageClassification"""),
    ("""image-segmentation""", """MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES""", """AutoModelForImageSegmentation"""),
    ("""fill-mask""", """MODEL_FOR_MASKED_LM_MAPPING_NAMES""", """AutoModelForMaskedLM"""),
    ("""object-detection""", """MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES""", """AutoModelForObjectDetection"""),
    (
        """zero-shot-object-detection""",
        """MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES""",
        """AutoModelForZeroShotObjectDetection""",
    ),
    ("""question-answering""", """MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES""", """AutoModelForQuestionAnswering"""),
    ("""text2text-generation""", """MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForSeq2SeqLM"""),
    ("""text-classification""", """MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForSequenceClassification"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES""", """AutoModelForSpeechSeq2Seq"""),
    (
        """table-question-answering""",
        """MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForTableQuestionAnswering""",
    ),
    ("""token-classification""", """MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForTokenClassification"""),
    ("""multiple-choice""", """MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES""", """AutoModelForMultipleChoice"""),
    (
        """next-sentence-prediction""",
        """MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES""",
        """AutoModelForNextSentencePrediction""",
    ),
    (
        """audio-frame-classification""",
        """MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForAudioFrameClassification""",
    ),
    ("""audio-xvector""", """MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES""", """AutoModelForAudioXVector"""),
    (
        """document-question-answering""",
        """MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForDocumentQuestionAnswering""",
    ),
    (
        """visual-question-answering""",
        """MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForVisualQuestionAnswering""",
    ),
    ("""image-to-text""", """MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES""", """AutoModelForVision2Seq"""),
    (
        """zero-shot-image-classification""",
        """MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForZeroShotImageClassification""",
    ),
    ("""depth-estimation""", """MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES""", """AutoModelForDepthEstimation"""),
    ("""video-classification""", """MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForVideoClassification"""),
    ("""mask-generation""", """MODEL_FOR_MASK_GENERATION_MAPPING_NAMES""", """AutoModelForMaskGeneration"""),
]



def 	_lowerCamelCase(	a				):
       __a       =    re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"   ,    a				)
       return [m.group(0				) for m in matches]



def 	_lowerCamelCase(	):
       __a       =    transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
       __a       =    {
           config.replace("Config"   ,    ""				): model_type for model_type, config in config_maping_names.items()
       }

       # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
       __a       =    collections.defaultdict(a				)
       __a       =    collections.defaultdict(a				)
       __a       =    collections.defaultdict(a				)

       # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
       for attr_name in dir(a				):
              __a       =    None
              if _re_tf_models.match(a				) is not None:
                     __a       =    tf_models
                     __a       =    _re_tf_models.match(a				).groups()[0]
              elif _re_flax_models.match(a				) is not None:
                     __a       =    flax_models
                     __a       =    _re_flax_models.match(a				).groups()[0]
              elif _re_pt_models.match(a				) is not None:
                     __a       =    pt_models
                     __a       =    _re_pt_models.match(a				).groups()[0]

              if lookup_dict is not None:
                     while len(a				) > 0:
                            if attr_name in model_prefix_to_model_type:
                                   __a       =    True
                                   break
                            # Try again after removing the last word in the name
                            __a       =    "".join(camel_case_split(a				)[:-1]				)

       __a       =    set(list(pt_models.keys()				) + list(tf_models.keys()				) + list(flax_models.keys()				)				)
       __a       =    list(a				)
       all_models.sort()

       __a       =    {"model_type": all_models}
       __a       =    [pt_models[t] for t in all_models]
       __a       =    [tf_models[t] for t in all_models]
       __a       =    [flax_models[t] for t in all_models]

       # Now let's use the auto-mapping names to make sure
       __a       =    {}
       for t in all_models:
              if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
                     __a       =    "AutoProcessor"
              elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
                     __a       =    "AutoTokenizer"
              elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
                     __a       =    "AutoFeatureExtractor"
              else:
                     # Default to AutoTokenizer if a model has nothing, for backward compatibility.
                     __a       =    "AutoTokenizer"

       __a       =    [processors[t] for t in all_models]

       return pd.DataFrame(a				)



def 	_lowerCamelCase(	a				):
       __a       =    [
           transformers_module.models.auto.modeling_auto,
           transformers_module.models.auto.modeling_tf_auto,
           transformers_module.models.auto.modeling_flax_auto,
       ]
       for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
              __a       =    [model_mapping, F"TF_{model_mapping}", F"FLAX_{model_mapping}"]
              __a       =    [auto_class, F"TF_{auto_class}", F"Flax_{auto_class}"]
              # Loop through all three frameworks
              for module, cls, mapping in zip(a   ,    a   ,    a				):
                     # The type of pipeline may not exist in this framework
                     if not hasattr(a   ,    a				):
                            continue
                     # First extract all model_names
                     __a       =    []
                     for name in getattr(a   ,    a				).values():
                            if isinstance(a   ,    a				):
                                   model_names.append(a				)
                            else:
                                   model_names.extend(list(a				)				)

            # Add pipeline tag and auto model class for those models
                     table.update({model_name: (pipeline_tag, cls) for model_name in model_names}				)

       return table



def 	_lowerCamelCase(	a   ,    a				):
       __a       =    get_frameworks_table()
       __a       =    Dataset.from_pandas(a				)

       __a       =    hf_hub_download(
           "huggingface/transformers-metadata"   ,    "pipeline_tags.json"   ,    repo_type="dataset"   ,    token=a				)
       __a       =    Dataset.from_json(a				)
       __a       =    {
           tags_dataset[i]["model_class"]: (tags_dataset[i]["pipeline_tag"], tags_dataset[i]["auto_class"])
           for i in range(len(a				)				)
       }
       __a       =    update_pipeline_and_auto_class_table(a				)

       # Sort the model classes to avoid some nondeterministic updates to create false update commits.
       __a       =    sorted(table.keys()				)
       __a       =    pd.DataFrame(
           {
               "model_class": model_classes,
               "pipeline_tag": [table[m][0] for m in model_classes],
               "auto_class": [table[m][1] for m in model_classes],
           }				)
       __a       =    Dataset.from_pandas(a				)

       with tempfile.TemporaryDirectory() as tmp_dir:
              frameworks_dataset.to_json(os.path.join(a   ,    "frameworks.json"				)				)
              tags_dataset.to_json(os.path.join(a   ,    "pipeline_tags.json"				)				)

              if commit_sha is not None:
                     __a       =    (
                         F"Update with commit {commit_sha}\n\nSee: "
                         F"https://github.com/huggingface/transformers/commit/{commit_sha}"
                     )
              else:
                     __a       =    "Update"

              upload_folder(
                  repo_id="huggingface/transformers-metadata"   ,    folder_path=a   ,    repo_type="dataset"   ,    token=a   ,    commit_message=a   ,    )



def 	_lowerCamelCase(	):
       __a       =    {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
       __a       =    transformers_module.pipelines.SUPPORTED_TASKS
       __a       =    []
       for key in pipeline_tasks:
              if key not in in_table:
                     __a       =    pipeline_tasks[key]["pt"]
                     if isinstance(a   ,    (list, tuple)				):
                            __a       =    model[0]
                     __a       =    model.__name__
                     if model not in in_table.values():
                            missing.append(a				)

       if len(a				) > 0:
              __a       =    ", ".join(a				)
              raise ValueError(
                  "The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside "
                  F"`utils/update_metadata.py`: {msg}. Please add them!"				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:List[str]									=			argparse.ArgumentParser()
   parser.add_argument("""--token""", type=str, help="""The token to use to push to the transformers-metadata dataset.""")
   parser.add_argument("""--commit_sha""", type=str, help="""The sha of the commit going with this update.""")
   parser.add_argument("""--check-only""", action="""store_true""", help="""Activate to just check all pipelines are present.""")
   SCREAMING_SNAKE_CASE__:Any									=			parser.parse_args()

   if args.check_only:
      check_pipeline_tags()
   else:
      update_metadata(args.token, args.commit_sha)
